Clues to Your Biological Age May Be Only Skin Deep

Researchers developing algorithms to predict differences in how people grow older

Clues to Your Biological Age May Be Only Skin Deep
Algorithms devised by Pew biomedical scholar Saket Navlakha and colleagues estimate a person’s age based on molecular data from skin cells.

Chronological age and biological age don’t always align. For example, a 60-year-old man may suffer from dementia and arthritis, while his 80-year-old neighbor may be in perfect health and training for her 10th marathon.

Can science better gauge how our bodies age and why we age differently? Pew biomedical scholar Saket Navlakha is studying the use of computer algorithms to analyze genetic markers in skin cells to reveal how our bodies weather the aging process. In the future, such predictions could inform and improve the health care that people receive as they grow older.

In December 2018, Navlakha, whose work bridges the worlds of big data and biomedicine, published research with colleagues at the Salk Institute for Biological Studies in the journal Genome Biology. In their work, the scientists extracted RNA, the genetic messaging molecules that direct cell activity within the body, from skin cells called dermal fibroblasts that were previously collected from 133 people ranging in age from 1 to 94. Then the team, based in La Jolla, California, tracked genetic changes that occur with age and compared data from different age groups. They next devised a computer algorithm that could identify genetic signatures and patterns in the data to predict the age of the subjects.

The algorithm created by Navlakha and his colleagues outperformed methods used in earlier studies, predicting the subjects’ ages with a median error of four years. They tested the validity of this approach using fibroblasts from a group with progeria, a rare genetic disorder characterized by rapidly accelerated biological aging. In the study, the progeria patients ranged in age from 2 to 8. The algorithm, however, predicted them to be about a decade older than their chronological age.

Navlakha’s group aims to use this algorithmic approach to further analyze how humans’ chronological and biological ages align and diverge, with the goal of developing methods to monitor and predict the onset of age-related illnesses. These tools could be valuable in caring for the increasing numbers of older people in the United States and around the globe. In the U.S., for example, the share of the population 65 years or older is expected to surpass that of children under the age of 5 by 2050.

With better understanding of the genetic signs of age-related disease, medical providers could identify potential problems earlier and more effectively target preventive treatment and counseling to safeguard seniors’ health.

Kara Coleman directs The Pew Charitable Trusts’ biomedical programs, including the biomedical scholars, Pew-Stewart Scholars for Cancer Research, and Latin American fellows programs. 

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